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Clinical decision latency, defined as the temporal interval from the moment actionable clinical data becomes available to the initiation of a corresponding therapeutic or diagnostic action, constitutes an under-recognized yet critical safety variable in contemporary healthcare delivery. Prevailing patient safety paradigms predominantly concentrate on categorical errors of commission or omission while largely treating time as an exogenous operational factor rather than an intrinsic propagative risk element capable of independently driving harm. This conceptual systems article reframes clinical decision latency as a primary, quantifiable, and governable safety variable. It proposes the clinical latency oversight lattice (CLOL)—an original infrastructural framework specifically designed to detect, quantify, assign accountability for, and interrupt harmful temporal delays across care pathways. Drawing on a targeted synthesis of literature that collectively addresses clinical decision support limitations, diagnostic uncertainty propagation, consequences of treatment delays, health IT-induced temporal vulnerabilities, and AI integration challenges, the manuscript argues that latency functions not as mere logistical inefficiency but as a dynamic, modality-sensitive, context-dependent risk multiplier. The CLOL architecture organizes temporal accountability into four interdependent lattice layers linked by a bidirectional feedback topology that enables real-time drift monitoring, explicit actor/system responsibility mapping, safety-variable score propagation, and orchestrated mitigation responses. Three interpretive mathematical expressions capture core dynamics: risk propagation across pathways, exponential decay of decision confidence under accumulating latency, and cumulative governance/monitoring burden. By institutionalizing latency as a traceable safety variable within a closed-loop accountability structure, CLOL offers healthcare analytics and AI system designers a theoretical and architectural foundation for shifting from retrospective error analysis toward prospective temporal harm prevention in high-stakes clinical environments.